Overview

Dataset statistics

Number of variables25
Number of observations1177
Missing cells342
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory230.0 KiB
Average record size in memory200.1 B

Variable types

Categorical12
Numeric13

Alerts

RBC is highly overall correlated with hematocritHigh correlation
hematocrit is highly overall correlated with RBCHigh correlation
CHD with no MI is highly imbalanced (57.8%)Imbalance
COPD is highly imbalanced (61.3%)Imbalance
BMI has 215 (18.3%) missing valuesMissing
heart rate has 13 (1.1%) missing valuesMissing
Systolic blood pressure has 16 (1.4%) missing valuesMissing
Diastolic blood pressure has 16 (1.4%) missing valuesMissing
Respiratory rate has 13 (1.1%) missing valuesMissing
temperature has 19 (1.6%) missing valuesMissing
SP O2 has 13 (1.1%) missing valuesMissing
Urine output has 36 (3.1%) missing valuesMissing
ID has unique valuesUnique

Reproduction

Analysis started2024-02-16 15:50:55.290728
Analysis finished2024-02-16 15:51:31.834488
Duration36.54 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

group
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
1
825 
2
352 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 825
70.1%
2 352
29.9%

Length

2024-02-16T10:51:32.072387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:32.213473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 825
70.1%
2 352
29.9%

Most occurring characters

ValueCountFrequency (%)
1 825
70.1%
2 352
29.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 825
70.1%
2 352
29.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 825
70.1%
2 352
29.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 825
70.1%
2 352
29.9%

ID
Real number (ℝ)

UNIQUE 

Distinct1177
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150778.12
Minimum100213
Maximum199952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:32.459286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100213
5-th percentile105999.4
Q1125603
median151901
Q3176048
95-th percentile195348.6
Maximum199952
Range99739
Interquartile range (IQR)50445

Descriptive statistics

Standard deviation29034.67
Coefficient of variation (CV)0.19256554
Kurtosis-1.22491
Mean150778.12
Median Absolute Deviation (MAD)25184
Skewness-0.005013361
Sum1.7746585 × 108
Variance8.4301203 × 108
MonotonicityNot monotonic
2024-02-16T10:51:32.789658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125047 1
 
0.1%
151165 1
 
0.1%
152862 1
 
0.1%
162107 1
 
0.1%
157076 1
 
0.1%
128113 1
 
0.1%
120123 1
 
0.1%
172068 1
 
0.1%
117121 1
 
0.1%
113198 1
 
0.1%
Other values (1167) 1167
99.2%
ValueCountFrequency (%)
100213 1
0.1%
100449 1
0.1%
100571 1
0.1%
100610 1
0.1%
100660 1
0.1%
100753 1
0.1%
100908 1
0.1%
100914 1
0.1%
101046 1
0.1%
101062 1
0.1%
ValueCountFrequency (%)
199952 1
0.1%
199925 1
0.1%
199912 1
0.1%
199861 1
0.1%
199859 1
0.1%
199803 1
0.1%
199786 1
0.1%
199745 1
0.1%
199677 1
0.1%
199603 1
0.1%

outcome
Categorical

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size9.3 KiB
0.0
1017 
1.0
159 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3528
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1017
86.4%
1.0 159
 
13.5%
(Missing) 1
 
0.1%

Length

2024-02-16T10:51:33.054772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:33.243451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1017
86.5%
1.0 159
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 2193
62.2%
. 1176
33.3%
1 159
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2352
66.7%
Other Punctuation 1176
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2193
93.2%
1 159
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 1176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3528
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2193
62.2%
. 1176
33.3%
1 159
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2193
62.2%
. 1176
33.3%
1 159
 
4.5%

age
Real number (ℝ)

Distinct68
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.055225
Minimum19
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:33.494697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile48
Q165
median77
Q385
95-th percentile89
Maximum99
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.434061
Coefficient of variation (CV)0.18140598
Kurtosis0.32536385
Mean74.055225
Median Absolute Deviation (MAD)9
Skewness-0.85627437
Sum87163
Variance180.47399
MonotonicityNot monotonic
2024-02-16T10:51:33.824820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 141
 
12.0%
84 48
 
4.1%
81 43
 
3.7%
83 40
 
3.4%
80 40
 
3.4%
78 39
 
3.3%
85 38
 
3.2%
79 35
 
3.0%
82 34
 
2.9%
87 33
 
2.8%
Other values (58) 686
58.3%
ValueCountFrequency (%)
19 2
0.2%
25 1
 
0.1%
28 1
 
0.1%
32 1
 
0.1%
35 4
0.3%
37 4
0.3%
38 3
0.3%
39 2
0.2%
40 2
0.2%
41 3
0.3%
ValueCountFrequency (%)
99 1
 
0.1%
98 1
 
0.1%
97 2
 
0.2%
96 5
0.4%
95 1
 
0.1%
94 2
 
0.2%
93 5
0.4%
92 5
0.4%
91 5
0.4%
90 6
0.5%

gendera
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
2
618 
1
559 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 618
52.5%
1 559
47.5%

Length

2024-02-16T10:51:34.119837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:34.324405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 618
52.5%
1 559
47.5%

Most occurring characters

ValueCountFrequency (%)
2 618
52.5%
1 559
47.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 618
52.5%
1 559
47.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 618
52.5%
1 559
47.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 618
52.5%
1 559
47.5%

BMI
Real number (ℝ)

MISSING 

Distinct933
Distinct (%)97.0%
Missing215
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean30.188278
Minimum13.346801
Maximum104.97037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:34.559239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.346801
5-th percentile19.692262
Q124.326461
median28.312474
Q333.633509
95-th percentile46.042797
Maximum104.97037
Range91.623565
Interquartile range (IQR)9.307048

Descriptive statistics

Standard deviation9.3259974
Coefficient of variation (CV)0.30892777
Kurtosis9.4689782
Mean30.188278
Median Absolute Deviation (MAD)4.5462028
Skewness2.2439622
Sum29041.123
Variance86.974228
MonotonicityNot monotonic
2024-02-16T10:51:35.061628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.29697566 3
 
0.3%
23.72453724 3
 
0.3%
27.98616708 2
 
0.2%
27.98989162 2
 
0.2%
25.21735858 2
 
0.2%
25.51686992 2
 
0.2%
20.78612058 2
 
0.2%
31.66902569 2
 
0.2%
22.7189744 2
 
0.2%
22.75346745 2
 
0.2%
Other values (923) 940
79.9%
(Missing) 215
 
18.3%
ValueCountFrequency (%)
13.34680089 1
0.1%
13.67362533 1
0.1%
13.77780533 1
0.1%
14.64480468 1
0.1%
14.85801788 1
0.1%
15.1953125 1
0.1%
15.81635816 1
0.1%
15.95256289 1
0.1%
15.96777387 1
0.1%
16.34814761 1
0.1%
ValueCountFrequency (%)
104.970366 1
0.1%
91.17665294 1
0.1%
83.26462934 1
0.1%
76.53061224 1
0.1%
70.84838859 1
0.1%
70.70854638 1
0.1%
69.96541771 1
0.1%
68.16131511 1
0.1%
65.60859683 1
0.1%
65.11721012 1
0.1%

hypertensive
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
1
845 
0
332 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 845
71.8%
0 332
 
28.2%

Length

2024-02-16T10:51:35.328734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:35.534630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 845
71.8%
0 332
 
28.2%

Most occurring characters

ValueCountFrequency (%)
1 845
71.8%
0 332
 
28.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 845
71.8%
0 332
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 845
71.8%
0 332
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 845
71.8%
0 332
 
28.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
646 
1
531 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 646
54.9%
1 531
45.1%

Length

2024-02-16T10:51:35.744916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:35.945358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 646
54.9%
1 531
45.1%

Most occurring characters

ValueCountFrequency (%)
0 646
54.9%
1 531
45.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 646
54.9%
1 531
45.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 646
54.9%
1 531
45.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 646
54.9%
1 531
45.1%

CHD with no MI
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1076 
1
 
101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1076
91.4%
1 101
 
8.6%

Length

2024-02-16T10:51:36.159249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:36.348022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1076
91.4%
1 101
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 1076
91.4%
1 101
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1076
91.4%
1 101
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1076
91.4%
1 101
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1076
91.4%
1 101
 
8.6%

diabetes
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
681 
1
496 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 681
57.9%
1 496
42.1%

Length

2024-02-16T10:51:36.569659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:36.770994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 681
57.9%
1 496
42.1%

Most occurring characters

ValueCountFrequency (%)
0 681
57.9%
1 496
42.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 681
57.9%
1 496
42.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 681
57.9%
1 496
42.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 681
57.9%
1 496
42.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
778 
1
399 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 778
66.1%
1 399
33.9%

Length

2024-02-16T10:51:36.959681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:37.149350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 778
66.1%
1 399
33.9%

Most occurring characters

ValueCountFrequency (%)
0 778
66.1%
1 399
33.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 778
66.1%
1 399
33.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 778
66.1%
1 399
33.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 778
66.1%
1 399
33.9%

depression
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1037 
1
140 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1037
88.1%
1 140
 
11.9%

Length

2024-02-16T10:51:37.366727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:37.571165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1037
88.1%
1 140
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 1037
88.1%
1 140
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1037
88.1%
1 140
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1037
88.1%
1 140
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1037
88.1%
1 140
 
11.9%

Hyperlipemia
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
730 
1
447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 730
62.0%
1 447
38.0%

Length

2024-02-16T10:51:37.790843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:37.979765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 730
62.0%
1 447
38.0%

Most occurring characters

ValueCountFrequency (%)
0 730
62.0%
1 447
38.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 730
62.0%
1 447
38.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 730
62.0%
1 447
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 730
62.0%
1 447
38.0%

Renal failure
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
747 
1
430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 747
63.5%
1 430
36.5%

Length

2024-02-16T10:51:38.205847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:38.401748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 747
63.5%
1 430
36.5%

Most occurring characters

ValueCountFrequency (%)
0 747
63.5%
1 430
36.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 747
63.5%
1 430
36.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 747
63.5%
1 430
36.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 747
63.5%
1 430
36.5%

COPD
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
1088 
1
 
89

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1177
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1088
92.4%
1 89
 
7.6%

Length

2024-02-16T10:51:38.621793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:51:38.810387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1088
92.4%
1 89
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 1088
92.4%
1 89
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1088
92.4%
1 89
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1088
92.4%
1 89
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1088
92.4%
1 89
 
7.6%

heart rate
Real number (ℝ)

MISSING 

Distinct1094
Distinct (%)94.0%
Missing13
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean84.575848
Minimum36
Maximum135.70833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:39.062655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile60.599185
Q172.37125
median83.610799
Q395.907143
95-th percentile112.78624
Maximum135.70833
Range99.708333
Interquartile range (IQR)23.535893

Descriptive statistics

Standard deviation16.018701
Coefficient of variation (CV)0.18940042
Kurtosis-0.301301
Mean84.575848
Median Absolute Deviation (MAD)11.682857
Skewness0.29975233
Sum98446.288
Variance256.5988
MonotonicityNot monotonic
2024-02-16T10:51:39.375106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 4
 
0.3%
61 4
 
0.3%
80 4
 
0.3%
88 4
 
0.3%
87 3
 
0.3%
83.7826087 3
 
0.3%
77 3
 
0.3%
91 3
 
0.3%
92 3
 
0.3%
74.8 3
 
0.3%
Other values (1084) 1130
96.0%
(Missing) 13
 
1.1%
ValueCountFrequency (%)
36 1
0.1%
39.32142857 1
0.1%
49 1
0.1%
49.69565217 1
0.1%
50.25 1
0.1%
50.55555556 1
0.1%
52.47058824 1
0.1%
52.72 1
0.1%
52.80952381 1
0.1%
52.81818182 1
0.1%
ValueCountFrequency (%)
135.7083333 1
0.1%
134.5652174 1
0.1%
133.0689655 1
0.1%
129.175 1
0.1%
129.125 1
0.1%
126.7222222 1
0.1%
126.6451613 1
0.1%
126.3793103 1
0.1%
126.28125 1
0.1%
125.7692308 1
0.1%

Systolic blood pressure
Real number (ℝ)

MISSING 

Distinct1102
Distinct (%)94.9%
Missing16
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean117.99504
Minimum75
Maximum203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:39.673891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile94.809524
Q1105.3913
median116.12821
Q3128.625
95-th percentile149.25926
Maximum203
Range128
Interquartile range (IQR)23.233696

Descriptive statistics

Standard deviation17.367618
Coefficient of variation (CV)0.1471894
Kurtosis0.63754218
Mean117.99504
Median Absolute Deviation (MAD)11.371795
Skewness0.7045228
Sum136992.24
Variance301.63417
MonotonicityNot monotonic
2024-02-16T10:51:39.984798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109 4
 
0.3%
117 3
 
0.3%
116.25 3
 
0.3%
110.25 3
 
0.3%
112 3
 
0.3%
118.962963 2
 
0.2%
121.7307692 2
 
0.2%
116.4090909 2
 
0.2%
121.9047619 2
 
0.2%
133.1428571 2
 
0.2%
Other values (1092) 1135
96.4%
(Missing) 16
 
1.4%
ValueCountFrequency (%)
75 1
0.1%
76.97619048 1
0.1%
79.23076923 1
0.1%
80.6 1
0.1%
82.45454545 1
0.1%
85.05882353 1
0.1%
85.28125 1
0.1%
85.35714286 1
0.1%
85.59090909 1
0.1%
85.59459459 1
0.1%
ValueCountFrequency (%)
203 1
0.1%
182.4848485 1
0.1%
180.6956522 1
0.1%
174.244898 1
0.1%
173.5652174 1
0.1%
173.4736842 1
0.1%
173.03125 1
0.1%
172.45 1
0.1%
169.8095238 1
0.1%
168.55 1
0.1%

Diastolic blood pressure
Real number (ℝ)

MISSING 

Distinct1077
Distinct (%)92.8%
Missing16
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean59.534497
Minimum24.736842
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:40.285684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.736842
5-th percentile43.678571
Q152.173913
median58.461538
Q365.464286
95-th percentile79.333333
Maximum107
Range82.263158
Interquartile range (IQR)13.290373

Descriptive statistics

Standard deviation10.684681
Coefficient of variation (CV)0.17947042
Kurtosis0.90991521
Mean59.534497
Median Absolute Deviation (MAD)6.6336996
Skewness0.60844373
Sum69119.55
Variance114.16241
MonotonicityNot monotonic
2024-02-16T10:51:40.558174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.45454545 5
 
0.4%
53 4
 
0.3%
61.5 3
 
0.3%
63.5 3
 
0.3%
64.75 3
 
0.3%
60.43478261 3
 
0.3%
63 3
 
0.3%
78.5 3
 
0.3%
58.45454545 3
 
0.3%
55.58333333 2
 
0.2%
Other values (1067) 1129
95.9%
(Missing) 16
 
1.4%
ValueCountFrequency (%)
24.73684211 1
0.1%
32.13043478 1
0.1%
32.80769231 1
0.1%
34.05555556 1
0.1%
34.53846154 1
0.1%
36.64516129 1
0.1%
36.86792453 1
0.1%
37.32258065 1
0.1%
37.38461538 1
0.1%
37.65217391 1
0.1%
ValueCountFrequency (%)
107 1
0.1%
101.5714286 1
0.1%
99.47619048 1
0.1%
98.73913043 1
0.1%
97.33333333 1
0.1%
95.90909091 1
0.1%
95.625 1
0.1%
95.36842105 1
0.1%
93.05660377 1
0.1%
89.95 1
0.1%

Respiratory rate
Real number (ℝ)

MISSING 

Distinct1004
Distinct (%)86.3%
Missing13
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean20.801511
Minimum11.137931
Maximum40.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:40.834005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.137931
5-th percentile14.794267
Q117.925694
median20.372308
Q323.3912
95-th percentile27.873558
Maximum40.9
Range29.762069
Interquartile range (IQR)5.4655053

Descriptive statistics

Standard deviation4.0029871
Coefficient of variation (CV)0.19243732
Kurtosis0.64265408
Mean20.801511
Median Absolute Deviation (MAD)2.6922581
Skewness0.54723027
Sum24212.959
Variance16.023906
MonotonicityNot monotonic
2024-02-16T10:51:41.115906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.8 5
 
0.4%
17.6 5
 
0.4%
19 5
 
0.4%
20 4
 
0.3%
21.4 4
 
0.3%
15 4
 
0.3%
26 4
 
0.3%
16.25 4
 
0.3%
20.25 3
 
0.3%
23.5 3
 
0.3%
Other values (994) 1123
95.4%
(Missing) 13
 
1.1%
ValueCountFrequency (%)
11.13793103 1
0.1%
11.81818182 1
0.1%
12 1
0.1%
12.21212121 1
0.1%
12.33333333 1
0.1%
12.36363636 1
0.1%
12.5 1
0.1%
12.6 1
0.1%
12.6969697 2
0.2%
12.70967742 1
0.1%
ValueCountFrequency (%)
40.9 1
0.1%
35.75 1
0.1%
35.26666667 1
0.1%
34.69343066 1
0.1%
33.85 1
0.1%
33.11538462 1
0.1%
33.10344828 1
0.1%
33.08 1
0.1%
32.44444444 1
0.1%
32.20833333 1
0.1%

temperature
Real number (ℝ)

MISSING 

Distinct775
Distinct (%)66.9%
Missing19
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean36.677286
Minimum33.25
Maximum39.132478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:41.427429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.25
5-th percentile35.795602
Q136.286045
median36.650794
Q337.021991
95-th percentile37.736111
Maximum39.132478
Range5.8824784
Interquartile range (IQR)0.73594581

Descriptive statistics

Standard deviation0.60755838
Coefficient of variation (CV)0.016564976
Kurtosis1.7372112
Mean36.677286
Median Absolute Deviation (MAD)0.36838584
Skewness0.13336885
Sum42472.297
Variance0.36912718
MonotonicityNot monotonic
2024-02-16T10:51:41.726225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.72222222 11
 
0.9%
36.66666667 10
 
0.8%
36.61111111 9
 
0.8%
36.77777778 9
 
0.8%
36.49074074 8
 
0.7%
36.27777778 8
 
0.7%
36.44444444 7
 
0.6%
36.52777778 7
 
0.6%
36.16666667 6
 
0.5%
36.88888889 6
 
0.5%
Other values (765) 1077
91.5%
(Missing) 19
 
1.6%
ValueCountFrequency (%)
33.25 1
0.1%
34.15072482 1
0.1%
34.32407407 1
0.1%
34.60555556 1
0.1%
34.67222256 1
0.1%
34.89351852 1
0.1%
35.08666667 1
0.1%
35.19444444 1
0.1%
35.26666667 1
0.1%
35.31481481 1
0.1%
ValueCountFrequency (%)
39.13247842 1
0.1%
39.08796296 1
0.1%
38.90522901 1
0.1%
38.79888889 1
0.1%
38.64351838 1
0.1%
38.53174603 1
0.1%
38.41358025 1
0.1%
38.36507937 1
0.1%
38.34615385 1
0.1%
38.27777757 1
0.1%

SP O2
Real number (ℝ)

MISSING 

Distinct866
Distinct (%)74.4%
Missing13
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean96.2729
Minimum75.916667
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:42.024911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75.916667
5-th percentile92.447009
Q195
median96.452273
Q397.9175
95-th percentile99.475753
Maximum100
Range24.083333
Interquartile range (IQR)2.9175

Descriptive statistics

Standard deviation2.2980019
Coefficient of variation (CV)0.023869665
Kurtosis6.6329697
Mean96.2729
Median Absolute Deviation (MAD)1.461943
Skewness-1.3839178
Sum112061.66
Variance5.2808126
MonotonicityNot monotonic
2024-02-16T10:51:42.339645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 10
 
0.8%
95.5 9
 
0.8%
95.75 7
 
0.6%
100 7
 
0.6%
95 7
 
0.6%
96.5 6
 
0.5%
96 6
 
0.5%
97.875 5
 
0.4%
97.5 5
 
0.4%
98 5
 
0.4%
Other values (856) 1097
93.2%
(Missing) 13
 
1.1%
ValueCountFrequency (%)
75.91666667 1
0.1%
83.06666667 1
0.1%
83.84615385 1
0.1%
87.46875 1
0.1%
88.42857143 1
0.1%
88.72222222 1
0.1%
89 1
0.1%
89.08333333 1
0.1%
89.11029412 1
0.1%
89.15384615 1
0.1%
ValueCountFrequency (%)
100 7
0.6%
99.95454545 1
 
0.1%
99.95 1
 
0.1%
99.93939394 1
 
0.1%
99.88571429 1
 
0.1%
99.86486486 1
 
0.1%
99.84615385 2
 
0.2%
99.82352941 1
 
0.1%
99.82142857 3
0.3%
99.82089552 1
 
0.1%

Urine output
Real number (ℝ)

MISSING 

Distinct810
Distinct (%)71.0%
Missing36
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1899.2765
Minimum0
Maximum8820
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:42.634880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile315
Q1980
median1675
Q32500
95-th percentile4260
Maximum8820
Range8820
Interquartile range (IQR)1520

Descriptive statistics

Standard deviation1272.3636
Coefficient of variation (CV)0.66992016
Kurtosis3.2212137
Mean1899.2765
Median Absolute Deviation (MAD)745
Skewness1.4082728
Sum2167074.5
Variance1618909.2
MonotonicityNot monotonic
2024-02-16T10:51:42.917950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2155 6
 
0.5%
1305 6
 
0.5%
930 5
 
0.4%
860 5
 
0.4%
1495 5
 
0.4%
2360 5
 
0.4%
750 5
 
0.4%
1625 4
 
0.3%
2685 4
 
0.3%
1715 4
 
0.3%
Other values (800) 1092
92.8%
(Missing) 36
 
3.1%
ValueCountFrequency (%)
0 2
0.2%
25 1
0.1%
30 1
0.1%
31 1
0.1%
33 1
0.1%
36 1
0.1%
73 1
0.1%
76 1
0.1%
90 1
0.1%
93 1
0.1%
ValueCountFrequency (%)
8820 1
0.1%
8760 1
0.1%
7925 1
0.1%
7400 1
0.1%
7350 1
0.1%
6875 1
0.1%
6865 1
0.1%
6700 1
0.1%
6670 1
0.1%
6635 1
0.1%

hematocrit
Real number (ℝ)

HIGH CORRELATION 

Distinct1056
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.914014
Minimum20.311111
Maximum55.425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:43.200263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.311111
5-th percentile25.221414
Q128.16
median30.8
Q335.0125
95-th percentile42.1025
Maximum55.425
Range35.113889
Interquartile range (IQR)6.8525

Descriptive statistics

Standard deviation5.2021023
Coefficient of variation (CV)0.1630037
Kurtosis0.85268118
Mean31.914014
Median Absolute Deviation (MAD)3.15
Skewness0.92354306
Sum37562.794
Variance27.061868
MonotonicityNot monotonic
2024-02-16T10:51:43.512943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.73333333 4
 
0.3%
30.6 4
 
0.3%
31.1 3
 
0.3%
28.2 3
 
0.3%
30.9 3
 
0.3%
37.6 3
 
0.3%
35.7 3
 
0.3%
27.43333333 3
 
0.3%
32.64 3
 
0.3%
31.26666667 3
 
0.3%
Other values (1046) 1145
97.3%
ValueCountFrequency (%)
20.31111111 1
0.1%
20.8 1
0.1%
21.15263158 1
0.1%
22.2 1
0.1%
22.8 1
0.1%
22.94545455 1
0.1%
23 1
0.1%
23.11111111 1
0.1%
23.4625 1
0.1%
23.55454545 1
0.1%
ValueCountFrequency (%)
55.425 1
0.1%
52.3 1
0.1%
51.47857143 1
0.1%
50.62 1
0.1%
49.35 1
0.1%
49.2 1
0.1%
48.9 1
0.1%
47.975 1
0.1%
47.65 1
0.1%
47.61428571 1
0.1%

RBC
Real number (ℝ)

HIGH CORRELATION 

Distinct1045
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5750098
Minimum2.03
Maximum6.575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:43.811890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.03
5-th percentile2.751
Q13.12
median3.49
Q33.9
95-th percentile4.7315
Maximum6.575
Range4.545
Interquartile range (IQR)0.78

Descriptive statistics

Standard deviation0.62683517
Coefficient of variation (CV)0.17533803
Kurtosis1.2802946
Mean3.5750098
Median Absolute Deviation (MAD)0.384
Skewness0.9306855
Sum4207.7866
Variance0.39292233
MonotonicityNot monotonic
2024-02-16T10:51:44.109390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.12 5
 
0.4%
3.3575 5
 
0.4%
3.715 4
 
0.3%
3.173333333 4
 
0.3%
2.86 4
 
0.3%
3.57 3
 
0.3%
4.33 3
 
0.3%
3.2425 3
 
0.3%
3.9 3
 
0.3%
2.94 3
 
0.3%
Other values (1035) 1140
96.9%
ValueCountFrequency (%)
2.03 1
0.1%
2.15 1
0.1%
2.216 1
0.1%
2.275555556 1
0.1%
2.34375 1
0.1%
2.37 1
0.1%
2.399 1
0.1%
2.407777778 1
0.1%
2.416363636 1
0.1%
2.47125 1
0.1%
ValueCountFrequency (%)
6.575 1
0.1%
6.154444444 1
0.1%
6 1
0.1%
5.8825 1
0.1%
5.847777778 1
0.1%
5.653333333 1
0.1%
5.646666667 2
0.2%
5.61 1
0.1%
5.592 1
0.1%
5.575 1
0.1%

MCH
Real number (ℝ)

Distinct926
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.539939
Minimum18.125
Maximum40.314286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2024-02-16T10:51:44.392027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.125
5-th percentile24.575556
Q128.25
median29.75
Q331.24
95-th percentile33.218095
Maximum40.314286
Range22.189286
Interquartile range (IQR)2.99

Descriptive statistics

Standard deviation2.6190535
Coefficient of variation (CV)0.088661439
Kurtosis1.3625001
Mean29.539939
Median Absolute Deviation (MAD)1.4928571
Skewness-0.63635231
Sum34768.508
Variance6.8594413
MonotonicityNot monotonic
2024-02-16T10:51:44.895206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.1 7
 
0.6%
29.4 6
 
0.5%
29.625 5
 
0.4%
29.7 5
 
0.4%
29.6 5
 
0.4%
31.7 5
 
0.4%
30.3 4
 
0.3%
31.2 4
 
0.3%
29.5375 4
 
0.3%
31.15 4
 
0.3%
Other values (916) 1128
95.8%
ValueCountFrequency (%)
18.125 1
0.1%
18.28 1
0.1%
20.575 1
0.1%
20.87 1
0.1%
20.88181818 1
0.1%
20.94 1
0.1%
20.9875 1
0.1%
21.04285714 1
0.1%
21.04444444 1
0.1%
21.325 1
0.1%
ValueCountFrequency (%)
40.31428571 1
0.1%
36.77142857 1
0.1%
36.46666667 1
0.1%
36.1875 1
0.1%
36.14285714 1
0.1%
36.04285714 1
0.1%
35.94 1
0.1%
35.55 1
0.1%
35.4 1
0.1%
35.33333333 1
0.1%

Interactions

2024-02-16T10:51:27.519403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:57.559595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:00.078831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:02.515291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:04.979644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:07.413086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:09.963539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:12.497058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:15.012395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:17.530644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:19.934719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:22.315632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:25.046514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:27.713289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:57.719853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:00.246840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:02.697701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:05.162220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:07.614309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:10.146444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:12.697200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:15.201695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:17.730113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:20.113388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:22.521509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:25.230928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:27.925659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:57.896960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:00.465124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:02.915190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:05.376711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:07.828870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:10.330973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:12.906111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:15.402400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:17.931375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:20.330191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:22.698868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:25.431927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:28.113539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:58.191133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:00.646295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:03.097641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:05.563772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:08.013207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:10.713222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:13.097917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:15.602655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:18.130073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:20.531908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:22.898701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:25.613056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:28.296807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:58.406629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:00.854721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:03.301757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:05.728891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:08.213612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:10.878641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:13.263304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:15.799714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:18.314478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:20.718520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:23.095894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:25.805392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:28.496549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:58.614375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:01.063449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:03.480973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:05.914207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:08.414498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:11.064597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:13.463546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:16.006587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:18.480114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:20.915347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:23.314002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:26.019988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:28.680118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:58.795740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:01.247066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:03.664850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:06.100050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:08.597616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:11.237208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:13.648275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:16.196778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:18.663472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:21.097693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:23.697352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:26.205090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:28.863486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:58.997775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:01.409334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:03.874255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:06.298914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:08.768232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:11.431910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:13.848513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:16.397135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:18.862356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:21.296609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:23.897441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:26.419035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:29.063707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:59.197195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:01.579647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:04.082575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:06.497664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:08.982315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:11.630343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:14.047520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:16.580988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:19.063481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:21.497120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:24.113398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:26.624684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:29.255134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:59.395594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:01.780294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:04.240981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:06.685089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:09.180049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:11.797829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:14.246682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:16.780563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:19.230760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:21.646220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:24.312293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:26.780379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:29.446898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:59.579017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:01.936297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:04.413409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:06.862702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:09.363528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:11.982219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:14.429865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:16.970641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:19.415656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:21.805588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:24.497311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:26.947925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:29.653522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:59.731306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:02.146849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:04.613843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:07.047861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:09.579864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:12.180059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:14.630278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:17.181066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:19.580390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:21.964626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:24.696534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:27.163761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:29.834123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:50:59.889513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:02.313298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:04.812319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:07.229904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:09.780995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:12.359763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:14.813750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:17.346690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:19.746881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:22.146632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:24.896765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:51:27.329751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-16T10:51:45.127613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BMICHD with no MICOPDDiastolic blood pressureHyperlipemiaIDMCHRBCRenal failureRespiratory rateSP O2Systolic blood pressureUrine outputageatrialfibrillationdeficiencyanemiasdepressiondiabetesgenderagroupheart ratehematocrithypertensiveoutcometemperature
BMI1.0000.0260.0920.1120.0000.053-0.0780.1010.091-0.094-0.1440.0730.206-0.3580.1040.0000.0000.2050.1420.000-0.0510.0720.0390.0970.087
CHD with no MI0.0261.0000.0000.0030.0340.0360.036-0.0010.0000.0150.049-0.0860.0100.0140.0000.0280.0300.0000.0700.000-0.0120.0060.0000.000-0.060
COPD0.0920.0001.0000.0400.000-0.0070.0390.0490.0680.034-0.1080.029-0.001-0.0230.0320.0000.0000.0650.0590.0000.0600.0730.0000.031-0.044
Diastolic blood pressure0.1120.0030.0401.0000.0480.044-0.0420.2790.0710.114-0.1090.3350.206-0.3070.0480.1160.0550.0000.1450.0460.3000.2700.0000.1050.003
Hyperlipemia0.0000.0340.0000.0481.000-0.0200.0060.0330.091-0.0290.007-0.019-0.0110.1030.0390.0000.0270.1280.0210.007-0.0710.0390.2220.042-0.074
ID0.0530.036-0.0070.044-0.0201.000-0.0200.0220.055-0.0140.0260.0390.048-0.0400.0250.0900.0750.0030.0210.0000.0210.0160.0810.000-0.003
MCH-0.0780.0360.039-0.0420.006-0.0201.000-0.3150.039-0.0270.036-0.021-0.0430.0840.0000.0750.1070.1550.0000.000-0.0280.0510.0000.0620.045
RBC0.101-0.0010.0490.2790.0330.022-0.3151.0000.1150.092-0.2250.0360.1650.0030.0740.3170.0430.0130.0990.0000.0050.8830.0000.098-0.079
Renal failure0.0910.0000.0680.0710.0910.0550.0390.1151.000-0.1150.0820.071-0.0580.0940.0330.1450.0000.1850.0920.000-0.219-0.1540.1890.102-0.081
Respiratory rate-0.0940.0150.0340.114-0.029-0.014-0.0270.092-0.1151.000-0.238-0.0780.050-0.0120.0000.0050.0000.0630.0000.0000.3550.0870.0840.1230.105
SP O2-0.1440.049-0.108-0.1090.0070.0260.036-0.2250.082-0.2381.000-0.025-0.0860.0600.0510.0500.0000.0530.0000.000-0.097-0.2280.0700.1080.030
Systolic blood pressure0.073-0.0860.0290.335-0.0190.039-0.0210.0360.071-0.078-0.0251.0000.227-0.0020.1140.0490.0000.1560.0510.055-0.1600.0230.1290.1160.083
Urine output0.2060.010-0.0010.206-0.0110.048-0.0430.165-0.0580.050-0.0860.2271.000-0.1910.1250.0000.0000.0530.1340.068-0.0160.1350.0750.1880.161
age-0.3580.014-0.023-0.3070.103-0.0400.0840.0030.094-0.0120.060-0.002-0.1911.0000.2950.1060.1060.1710.0330.000-0.1940.0530.1990.060-0.189
atrialfibrillation0.1040.0000.0320.0480.0390.0250.0000.0740.0330.0000.0510.1140.1250.2951.0000.0910.0480.0000.0200.000-0.0030.0400.0000.094-0.133
deficiencyanemias0.0000.0280.0000.1160.0000.0900.0750.3170.1450.0050.0500.0490.0000.1060.0911.0000.0540.0520.0740.000-0.042-0.3750.0000.0920.008
depression0.0000.0300.0000.0550.0270.0750.1070.0430.0000.0000.0000.0000.0000.1060.0480.0541.0000.0000.0730.0000.053-0.0030.0290.0490.022
diabetes0.2050.0000.0650.0000.1280.0030.1550.0130.1850.0630.0530.1560.0530.1710.0000.0520.0001.0000.0180.000-0.132-0.0780.1240.0370.033
gendera0.1420.0700.0590.1450.0210.0210.0000.0990.0920.0000.0000.0510.1340.0330.0200.0740.0730.0181.0000.000-0.011-0.0780.0000.000-0.009
group0.0000.0000.0000.0460.0070.0000.0000.0000.0000.0000.0000.0550.0680.0000.0000.0000.0000.0000.0001.0000.0050.0450.0410.000-0.027
heart rate-0.051-0.0120.0600.300-0.0710.021-0.0280.005-0.2190.355-0.097-0.160-0.016-0.194-0.003-0.0420.053-0.132-0.0110.0051.000-0.0100.1190.1180.156
hematocrit0.0720.0060.0730.2700.0390.0160.0510.883-0.1540.087-0.2280.0230.1350.0530.040-0.375-0.003-0.078-0.0780.045-0.0101.0000.0130.000-0.078
hypertensive0.0390.0000.0000.0000.2220.0810.0000.0000.1890.0840.0700.1290.0750.1990.0000.0000.0290.1240.0000.0410.1190.0131.0000.0630.022
outcome0.0970.0000.0310.1050.0420.0000.0620.0980.1020.1230.1080.1160.1880.0600.0940.0920.0490.0370.0000.0000.1180.0000.0631.000-0.056
temperature0.087-0.060-0.0440.003-0.074-0.0030.045-0.079-0.0810.1050.0300.0830.161-0.189-0.1330.0080.0220.033-0.009-0.0270.156-0.0780.022-0.0561.000

Missing values

2024-02-16T10:51:30.142998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-16T10:51:30.897011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-16T10:51:31.487953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

groupIDoutcomeagegenderaBMIhypertensiveatrialfibrillationCHD with no MIdiabetesdeficiencyanemiasdepressionHyperlipemiaRenal failureCOPDheart rateSystolic blood pressureDiastolic blood pressureRespiratory ratetemperatureSP O2Urine outputhematocritRBCMCH
011250470.072137.58817900011011068.837838155.86666768.33333316.62162236.71428698.3947372155.026.2727272.96000028.250000
111398120.0752NaN000010001101.370370140.00000065.00000020.85185236.68254096.9230771425.030.7800003.13800031.060000
211097870.083226.57263400001001072.318182135.33333361.37500023.64000036.45370495.2916672425.027.7000002.62000034.320000
311305870.043283.26462900000000094.500000126.40000073.20000021.85714336.28703793.8461548760.036.6375004.27750026.062500
411382900.075231.82484210001001167.920000156.56000058.12000021.36000036.76190599.2800004455.029.9333333.28666730.666667
511546530.076124.26229311001011174.181818118.10000052.95000020.54545535.26666796.8181821840.027.3333333.23500026.566667
611944200.072139.66742610000011169.636364106.56521747.82608719.14814835.60317595.6363642450.028.9375003.72000024.337500
711534610.083222.31111111011000084.666667141.13043546.91304318.40000036.67361197.8750003039.028.8000002.86714333.214286
811130760.061219.99224311010001091.91666798.43478352.65217418.58333337.10317598.0416671625.031.2411763.41750029.193750
911472520.067145.03203010010000075.083333122.00000056.75000018.12500036.86111194.4583336107.030.3000003.27000029.783333
groupIDoutcomeagegenderaBMIhypertensiveatrialfibrillationCHD with no MIdiabetesdeficiencyanemiasdepressionHyperlipemiaRenal failureCOPDheart rateSystolic blood pressureDiastolic blood pressureRespiratory ratetemperatureSP O2Urine outputhematocritRBCMCH
116721872720.083123.46978311110001063.444444101.46153945.57692321.44444436.67777899.481481NaN31.0000003.21800027.860000
116821248801.080128.94250711011011071.458333108.88000058.08000015.66666737.05555683.066667NaN26.8600003.18000026.177778
116921989330.088226.571482100110100110.166667120.38095262.04761925.08333336.80000093.333333805.030.5636364.17545523.300000
117021879910.079124.221453110000100110.478261117.95454582.68181820.91304336.44444494.5000001825.028.8200003.06375030.850000
117121516360.092123.82216910000001073.619048129.52380951.19047619.80952436.21296397.761905NaN39.3600004.64400025.920000
117221711300.062125.516870110101110100.125000142.54545569.68181824.50000037.05555694.130435NaN33.6285713.68571427.842857
117321016590.078125.822710010101110114.640000101.22222251.88888918.95833335.97222293.526316NaN28.7157893.27000029.772727
117421620690.085223.89177911011001052.720000137.79166740.95833318.68000036.77777898.800000118.027.6857143.06571426.900000
117521209670.079235.28855400111111093.40000094.41666762.45833322.04000036.42222299.5600002585.033.5375003.44625031.150000
117621076360.047123.12138410010001067.289855131.52307772.27692323.46478937.71666799.652174NaN29.9375002.79714336.771429